惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
博客园 - Franky
T
Tailwind CSS Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
月光博客
月光博客
人人都是产品经理
人人都是产品经理
雷峰网
雷峰网
Hugging Face - Blog
Hugging Face - Blog
有赞技术团队
有赞技术团队
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
阮一峰的网络日志
阮一峰的网络日志
C
Check Point Blog
爱范儿
爱范儿
T
The Blog of Author Tim Ferriss
aimingoo的专栏
aimingoo的专栏
Stack Overflow Blog
Stack Overflow Blog
博客园 - 聂微东
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
L
LangChain Blog
云风的 BLOG
云风的 BLOG
MyScale Blog
MyScale Blog
Microsoft Security Blog
Microsoft Security Blog
The Cloudflare Blog
博客园 - 三生石上(FineUI控件)

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Transforming the Erdős-Kac theorem
Kimihiro Noguchi · 2025-06-10 · via math.ST updates on arXiv.org

Transforming the Erdős-Kac theorem provides more flexibility in how the theorem can be utilized as an interval estimate for the prime omega function, which counts the number of distinct prime divisors. Here, we consider a direct transformation by the delta method. Then, we demonstrate that the square-root and three-quarters power asymptotically achieve variance stabilization and an optimal width, respectively. Furthermore, by adjusting the denominator of the theorem, we derive the score interval estimate. To make these interval estimates reliable for small positive integers, we examine performances of various interval estimates for the prime omega function using fuzzy coverage probabilities. The results indicate that the score interval estimate performs well even for small positive integers after training the mean and standard deviation using the prime omega function. Moreover, the Poisson interval estimate is relatively reliable with or without training. Additional theoretical results on the Erdős-Pomerance theorem are also provided.